ChainingOneAlgorithm

This classe intend to run many algorithms parallely on a local system for medium size datasets, it works for one version of an algorithm with various parameters

O: the raw object from which vectorizations came from
V: the nature of the working vector
Cz: a clusterizable descendant, EasyClusterizable is the basic advise instance
GS: the nature of the collection containing Cz[O, V]
Vecto: the current vectorization which gives the current Vector nature
CM: the clustering model type corresponding to the nature of the given algorithm type
Algorithms: the clustering algorith type

Linear Supertypes

ClusteringSharedTypes, Serializable, Serializable, AnyRef, Any

Known Subclasses

LocalClusteringChainingBinary, LocalClusteringChainingScalar

Type Members

final type ClusterID = Int

Definition Classes
ClusteringSharedTypes

Abstract Value Members

abstract val algorithms: Seq[Algorithms]

Seq the algorithm which will be launched with various parameters
abstract val data: GS[Cz[O, V]]

the dataset of clusterizable
abstract val isDatasetSortedByID: Boolean

Necessary security for specific algorithm which require to have same data order
abstract val modelsKeeper: ModelsKeeper
abstract val vectorizations: Seq[Vecto]

the current vectorization employed

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final val algorithmNature: ClusteringAlgorithmNature
final val algorithmsIndices: Seq[Seq[Int]]

Indices of launched algorithm, ie (previousClusteringNumber until previousClusteringNumber + algorithms.size)
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final val clusteringIDsResults: ParSeq[GS[ClusterID]]

ParSeq of Collection of ClusterID, each ParSeq elem correspond to the given algorithms order
final val dataSortedByID: GS[Cz[O, V]]
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getAllModels: Seq[CM]

get all models resulting from given clustering algorithm
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getEveryMIPerClustering(groundTruth: GenSeq[Int]): GenSeq[(Double, Double, Double)]
final def getModel(i: Int): Option[CM]

get one model from those resulting from given clustering algorithm
final def getModelsFromVecto(vecto: Vecto): Seq[CM]

get all models resulting from given clustering algorithm for a particular vectorization
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final val modelMapping: ModelsMapping[Int, CM]

Model Mapping required to access to specific model in ModelsKeeper HMAP
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def obtainSortedClusterizedData: GS[Cz[O, V]]

The Dataset vectorization is the original one
The Dataset vectorization is the original one
returns
the linked dataset between clusterizable and their associate clusteringIDs sorted by clusterizable ID
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package chaining

trait ChainingOneAlgorithm[O, V <: GVector[V], Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz], GS[X] <: GenSeq[X], Vecto <: VectorizationGenLocal[O, V, Vecto], CM <: ClusteringModelLocal[V], Algorithms <: ClusteringAlgorithmLocal[V, CM]] extends ClusteringSharedTypes

Type Members

final type ClusterID = Int

Abstract Value Members

abstract val algorithms: Seq[Algorithms]

abstract val data: GS[Cz[O, V]]

abstract val isDatasetSortedByID: Boolean

abstract val modelsKeeper: ModelsKeeper

abstract val vectorizations: Seq[Vecto]

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final val algorithmNature: ClusteringAlgorithmNature

final val algorithmsIndices: Seq[Seq[Int]]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final val clusteringIDsResults: ParSeq[GS[ClusterID]]

final val dataSortedByID: GS[Cz[O, V]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getAllModels: Seq[CM]

final def getClass(): Class[_]

final def getEveryMIPerClustering(groundTruth: GenSeq[Int]): GenSeq[(Double, Double, Double)]

final def getModel(i: Int): Option[CM]

final def getModelsFromVecto(vecto: Vecto): Seq[CM]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final val modelMapping: ModelsMapping[Int, CM]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def obtainSortedClusterizedData: GS[Cz[O, V]]

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from ClusteringSharedTypes

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped